Chunking

Fast Take: Chunking breaks documents into smaller pieces so AI can retrieve the right context instead of the whole file.

Layer: Retrieval Status: Mature Last Updated: 2026-01-06

Decision Box

✅ Use this when:

You’re building RAG
Documents are longer than a few paragraphs
You need precise retrieval
Context window limits matter

❌ Ignore this when:

Data is already short and atomic
You’re not doing retrieval
You’re working with structured tables only

⚠️ Risk if misused:

Chunks too large → irrelevant context
Chunks too small → loss of meaning
No overlap → broken ideas
Bad chunking ruins embeddings before search even happens

Simple Explanation

⚠️ What it is:

Chunking is the process of splitting content into smaller, meaningful sections before embedding and storage.

Analogy:
It’s like cutting a textbook into indexed flashcards instead of forcing AI to flip through the whole book every time.

Why it matters:

Retrieval quality depends more on chunking than the model itself.

Technical Breakdown

Key Concepts:

Fixed-size chunking
Semantic chunking
Recursive chunking
Sliding window with overlap

Implementation Snapshot:

Chunk size (tokens or characters)
Overlap size
Structure awareness (headings, paragraphs)
Metadata attachment

Common Failure Modes:

Ignoring document structure
Using one chunk size for all content
No overlap between chunks
Chunking before cleaning the data

Cost Reality:

Cost profile: Medium

Cost profile: Low
Main impact: retrieval accuracy, not compute cost

Top Players

Company / Tool – why it matters here:

LlamaIndex
LangChain
Unstructured
Haystack
Custom preprocessors

Go Deeper

Appears in:

AI Foundations for Builders — Module 2: The Library

This concept is covered in Module 2 – The Library (RAG & Vector Databases)

Term Flow

Prerequisites:

Embeddings
Vector Databases

Next Concepts:

Semantic Search
Top-K Retrieval
Re-ranking

Often Confused With:

Tokenization
Parsing
File splitting